Visualize your data with a new set of charts: Treemap, Butterfly, Sankey, and Cluster charts in Zoho CRM
Dear customers,
When it comes to analytics, it's not just about the numbers; it's about understanding the story behind them.
With that philosophy in mind, we’re excited to introduce a line of representations to the existing array of chart types in Zoho CRM: Treemap, butterfly, sankey and cluster charts. Let's go!
Treemaps
Treemap charts are used to visually represent hierarchical datasets in a rectangular layout. It aligns its parent categories as larger tiles with their sub-categories nested within them. The size of each tile is proportional to its corresponding value, making it easy to compare different segments within the hierarchy.
This is how a treemap chart looks:
These structured data representations help you understand overall performance and contributions, as well as compare participating entities at a glance.
Unlike traditional bar charts or pie charts, treemaps can be used if your datasets are large and exhibit parent-child relationships.
Here are some examples to better understand their usage:
Comparing revenue distribution between functions
A company's revenue is distributed among its functions before it gets further disbursed to its employees. Treemap charts can be used to depict this distribution and compare it between functions. As you can see below, the hierarchy can be represented as a treemap to compare it directly with other functions:
The hierarchy at the top shows just the numbers and levels, but the treemap chart represents the numbers proportionally, allowing leaders to visualize the difference in distribution.
Interpretation: As you can easily see in the treemap, sales and marketing receive the same amount of revenue, while engineering is given significantly more than the other two.
Likewise, with treemap charts, you can:
- Compare popular lead sources with lead counts is the measure and lead source being the participating entity—a classic single-grouping configuration.
- View cost savings achieved across departments. With the departments as the grouping parameters and the cost saved as the measured amount, the chart lays out all the departments as tiles in proportion, based on money saved.
- Compare ad spends across channels, where channels are the parent grouping and ad spend is the measured unit.
Butterfly
Butterfly charts are used to compare two related datasets side-by-side, resulting in a representation that looks like a butterfly or tornado.
Now, how does it differ from bar charts?
The standard bar chart can compare two entities for a given measure. Say, you are comparing the performance of Mary and Charles. The two users' data is represented using bars, and the length will denote their performances. But, when it comes to comparing their performances over a period or their contribution across different stages, a bar chart is not sufficient.
With butterfly charts, you can:
- Compare revenue between two of your branches each month. With branches being compared for sum of sales revenue, grouped by closing date.
- Compare the performance of two reps in a given quarter. Compared between two users for average of amount of deals, grouped by closing date until today.
In addition to the user-based comparison above, butterfly charts are well-suited to visualizing other types of data comparisons, like:
- Picklist-based comparisons
- Duration-based comparisons
- Aggregate-based comparisons
Business scenario
Comparing the number of deals closed for each lead sources: Duration-based comparison
You can identify the productive lead source by comparing the number of deal closures for every lead source in your organization based on their closing week.
Analyzing effective sales methodology, inbound vs. outbound: Picklist-based comparison
Businesses use both inbound and outbound lead generation strategies, and each of these methods can reap different results based on the season and occasion. By comparing inbound versus outbound each month, you can identify which works best at what time.
Analyze the amount versus the expected revenue between accounts: Aggregate-based comparison
Expected revenue is a result of a deal's progression in the sales pipeline. Comparing the amount versus their expected revenue will not only help visualize the expected revenue of participating accounts but also indicate the accounts' stage in the sales pipeline.
Sankey
A Sankey Chart is designed to visualize the movement of data across different data groups. Unlike traditional charts—such as bar, column, pie, or donut—that mainly provide a static distribution of values, the Sankey Chart focuses on illustrating the flow between multiple segments or grouping fields. This makes it an ideal choice when you want to track how values (like lead counts, revenue, or deal statuses) move from one category to the next.
Key features
- Flow visualization: With the Sankey Chart, you can observe the movement of data between different groups.
- Multiple grouping fields: This chart works best when you have at least two grouping fields. You can go even further and add a third grouping to see an even more detailed mapping of your data flow.
- Simple configuration: The configuration for the Sankey Chart is as simple as any other chart type in Analytics.
Business scenarios:
Imagine you’re a sales manager trying to get a better handle on your team’s performance and your company’s pipeline. You want to understand not just how many deals are coming in, but also which sources are contributing the most value—and how those deals are progressing through different sales stages.
Let’s say you want to understand which lead sources are driving the most deal activity and how those deals progress through the pipeline. You can create a Sankey chart that maps the count of deals from Lead Source to Stage.
After analyzing the chart, you might notice that Online Store brings in a high volume of early-stage deals, while sources like External Referral contribute fewer deals that are more likely to reach advanced stages like Proposal or Negotiation.
This insight helps you prioritize nurturing the most profitable channels.
Sankey charts can also be helpful in other operational scenarios where understanding transitions across stages or teams is essential:
- Regional revenue distribution: Visualize how revenue flows across different regions, product categories, and their corresponding annual revenue. This helps you compare which regions contribute the most to each product line and where your high-value segments lie.
- Ticket handling flow: Visualize the flow of support tickets from their origin channel to internal departments and finally to resolution statuses. This can reveal workload imbalances or common points of delay in your support process.
Cluster
A cluster chart is similar to stacked column charts, but instead of stacking horizontally, the data is represented as vertical bars. As you create a column chart with multiple groupings, you can change the type of column chart to a cluster chart to achieve this representation.
In the above image, you can see the stacked column chart compares the number of lead conversions based on popular sources between countries. The stacks appearing on top of existing stacks ask you to calibrate the record count (y-axis) based on the previous stacks, which can lead to inaccurate interpretations. In this case, a cluster representation will paint a clearer picture of the analyses.
Other minor enhancements:
In addition to the three charts we mentioned above, we've also made the following minor changes:
- Display total summary: Thus far, for all charts, each participating measure included labels. Now, to better understand overall contributions, a check box to display the total summary is provided under More options on the Chart Configuration page. Based on the configuration, the total revenue or the rolled-up quantities will be prominently displayed.
- Merge Y-axis: For charts that use two y-axis measurements, the intention is to view the progression of one entity against these two measures. Despite the scale, if the y-axis on the left is disproportionate to the values of the y-axis on the right, the plotted graph will result in a graphically and logically incorrect representation.
As you can see in the image below, the plot area of the sum of amount bar and the sum of expected revenue aligns close together, while, the difference between $700,000 and $40,500 is drastic, creating inaccurate interpretations.
In this enhancement, we're allowing neighboring values of measures to merge so that the interpretation can be more visually accurate.
- Clone components to a different dashboard: Dashboards in Analytics serve in unique ways for various audiences—there can be separate dashboards for the sales team, marketing team, engineering team, and so on, and the chances of using the same measure for reference is common. Thus, when you clone a chart, you can now determine the target dashboard in which the cloned chart can be placed.
Resource: Help document
Thanks and have a good one!
Kind regards,
Saranya Balasubramanian
Saranya Balasubramanian
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